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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Digital Zone: Jurnal Teknologi Informasi dan Komunikasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Informasi dan Pendidikan Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Computer Scine and Information Technology Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat
Yuhandri Yuhandri, Yuhandri
Unknown Affiliation
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Other

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Journal : Perfecting a Video Game with Game Metrics

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An artificial neural network approach for detecting skin cancer Sugiarti Sugiarti; Yuhandri Yuhandri; Jufriadif Na'am; Dolly Indra; Julius Santony
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.9547

Abstract

This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.
Co-Authors Afifah Cahayani Adha Agus Perdana Windarto Akbar Iskandar Aldi Muharsyah Andrean, Fajri Ilhami Anita Sindar Arif Budiman Aulia, Allans Prima Budayawan, Khairi Chandra, Mrs Montesna Dewi Eka Putri Dikki Handoko Dolly Indra Dwi Narulita Dwika Assrani Efori Buulolo Eka Praja Wiyata Mandala Fauzan, Yuniko Febri Hadi Feri Irawan Firzada, Fahmi Fuad El Khair Gemilang, Fhajri Arye Gunadi Widi Nurcahyo Hartomi, Zupri Henra Hendrick, H Heri Nurdiyanto Iftitah, Hasanatul Jaya, Budi Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Kadrahman, Kadrahman Kurniawan, Jefdy Lidia K Simanjuntak Liga Mayola M Ikhsan Setiawan M, Mutia Maharani Maharani, Maharani Mesran, Mesran Musli Yanto Na'am, Jufriadif Natalia Silalahi, Natalia Nelly Astuti Hasibuan Nopi Purnomo Nuning Kurniasih Petti Indrayati Sijabat Pohan, Yosua Ade Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putra, Yendi Putri, Stefani Rahmad Dian Rakhmad Kuswandhie Rio Andika Malik Ronda Deli Sianturi S Sumijan Sagala, Gamrina Salmiati, S Sarjon Defit Sarjon Defit Sisi Hendriani Siska, Ayu Prima Soraya Rahma Hayati Sri Dewi Stephano, Rivo Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Darma Nasution Sutiksno, Dian Utami Tessa Y M Sihite Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Winanda, Teddy Yeni, Nasma Yolla Rahmadi Helmi Yudha Aditya Fiandra Zikir Risky, Muhammad Arif